ArticleEuropean radiology experimental2022
A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data.
Article in European radiology experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.World journal of emergency surgery : WJES · 2025Article
- Modeling Upper Limb Rehabilitation-Induced Recovery After Stroke: The Role of Attention as a Clinical Confounder.Physical therapy · 2025Article
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- Vocal biomarkers in heart failure-design, rationale and baseline characteristics of the AHF-Voice study.Frontiers in digital health · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- The prevalence and correlates of falls in adults with attention-deficit/hyperactivity disorder: cross-sectional study.Frontiers in psychiatry · 2025Article
- Clinical prediction model for red cell blood transfusion in elective primary posterior lumbar spine fusion.Scientific reports · 2024Article
- The role of intimate partner violence perpetrators' resting state functional connectivity in treatment compliance and recidivism.Scientific reports · 2024Article
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7 authors.
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Abstract
backgroundEstimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study.
methodsThree different methods (Riley's; "rule of thumb" with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated.
resultsTo develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, by including direct measures of the outcome to be predicted and/or by increasing the follow-up period. For model validation, the estimated sample size resulted to be 326 patients for neuroblastoma, 246 for high-risk neuroblastoma, and 592 for DIPG.
conclusionsGiven the variability of the different sample sizes obtained, we recommend using methods based on epidemiological data and the nature of the results, as the results are tailored to the specific clinical problem. In addition, sample size can be reduced by lowering the number of parameter predictors, by including direct measures of the outcome of interest.
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